code: def test_pca_95percent(sparse_data_transformed):
      (
          X_transformed,
          y,
          categorical_transformed,
      ) = sparse_data_transformed
      mf = meta_features.metafeatures[
          "PCAFractionOfComponentsFor95PercentVariance"
      ](X_transformed, y, logging.getLogger("Meta"))
      assert pytest.approx(0.7741935483870968) == mf.value
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 17 for calling a global function with example usage: test_pca_95percent(sparse_data_transformed) without return types